Effect of hydrogen peroxide on antibacterial activities of Canadian honeys
Bibliographic record
Abstract
Honey is recognized as an efficacious topical antimicrobial agent in the treatment of burns and wounds. The antimicrobial activity in some honeys depends on the endogenous hydrogen peroxide content. This study was aimed to determine whether honey's hydrogen peroxide level could serve as a honey-specific, activity-associated biomarker that would allow predicting and assessing the therapeutic effects of honey. Using a broth microdilution assay, I analyzed antibacterial activities of 42 Canadian honeys against two bacterial strains: Escherichia coli (ATCC 14948) and Bacillus subtilis (ATCC 6633). The MIC90 and MIC50 were established from the dose-response relationship between antibacterial activities and honey concentrations. The impact of H2O2 on antibacterial activity was determined (i) by measuring the levels of H2O2 before and after its removal by catalase and (ii) by correlating the results with levels of antibacterial activities. Canadian honeys demonstrated moderate to high antibacterial activity against both bacterial species. Both MIC90 and MIC50 revealed that the honeys exhibited a selective growth inhibitory activity against E. coli, and this activity was strongly influenced by endogenous H2O2 concentrations. Bacillus subtilis activity was marginally significantly correlated with H2O2 content. The removal of H2O2 by catalase reduced the honeys' antibacterial activity, but the enzyme was unable to completely decompose endogenous H2O2. The 25%-30% H2O2 "leftover" was significantly correlated with the honeys' residual antibacterial activity against E. coli. These data indicate that all Canadian honeys exhibited antibacterial activity, with higher selectivity against E. coli than B. subtilis, and that these antibacterial activities were correlated with hydrogen peroxide production in honeys. Hydrogen peroxide levels in honey, therefore, is a strong predictor of the honey's antibacterial activity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".